


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class 							lowerCamelCase__						(				UpperCAmelCase       ,						UpperCAmelCase       ,						UpperCAmelCase   ):






			"""simple docstring"""






			@register_to_config
			def __init__(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case = False   ,    ):

						'''simple docstring'''


						super().__init__()

						UpperCamelCase__       =							nn.Embedding(snake_case   ,    snake_case  )

						UpperCamelCase__       =							nn.Embedding(snake_case   ,    snake_case  )
						UpperCamelCase__       =							False

						UpperCamelCase__       =							nn.Dropout(p=snake_case  )

						UpperCamelCase__       =							TaConfig(
						    vocab_size=snake_case   ,    d_model=snake_case   ,    num_heads=snake_case   ,    d_kv=snake_case   ,    d_ff=snake_case   ,    dropout_rate=snake_case   ,    feed_forward_proj=snake_case   ,    is_decoder=snake_case   ,    is_encoder_decoder=snake_case   ,    )

						UpperCamelCase__       =							nn.ModuleList()
						for lyr_num in range(snake_case  ):
									UpperCamelCase__       =							TaBlock(snake_case  )
									self.encoders.append(snake_case  )

						UpperCamelCase__       =							TaLayerNorm(snake_case  )
						UpperCamelCase__       =							nn.Dropout(p=snake_case  )







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.token_embedder(snake_case  )

						UpperCamelCase__       =							encoder_input_tokens.shape[1]
						UpperCamelCase__       =							torch.arange(snake_case   ,    device=encoder_input_tokens.device  )
						x += self.position_encoding(snake_case  )

						UpperCamelCase__       =							self.dropout_pre(snake_case  )

						# inverted the attention mask
						UpperCamelCase__       =							encoder_input_tokens.size()
						UpperCamelCase__       =							self.get_extended_attention_mask(snake_case   ,    snake_case  )

						for lyr in self.encoders:
									UpperCamelCase__       =							lyr(snake_case   ,    snake_case  )[0]
						UpperCamelCase__       =							self.layer_norm(snake_case  )

						return self.dropout_post(snake_case  ), encoder_inputs_mask






def 							UpperCamelCase_(					_A :Union[str, Any]	)->		List[str]:
			UpperCamelCase__       =							[0] * len(_A	)
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							0

			for values in graph.values():
						for i in values:
									indegree[i] += 1

			for i in range(len(_A	)	):
						if indegree[i] == 0:
									queue.append(_A	)

			while queue:
						UpperCamelCase__       =							queue.pop(0	)
						cnt += 1
						topo.append(_A	)
						for x in graph[vertex]:
									indegree[x] -= 1
									if indegree[x] == 0:
												queue.append(_A	)

			if cnt != len(_A	):
						print("Cycle exists"	)
			else:
						print(_A	)


# Adjacency List of Graph
__UpperCamelCase     		=							{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)


